Our Europe GDP data is released before official statistics, and provides higher frequency insights.
This is a preview for 2015 across ~1300 EU NUTS3 regions with monthly frequency.
We have data in Europe from 2014 – present.
Press the play button on the interactive 3D map to see how GDP changed. Click, hold, and drag the right mouse button to change viewing angle. Scroll to zoom in.
Customers/ partners/ affiliations:
Some examples of how our Europe data has been used by customers/ partners across industries.
We compared our models’ prediction of annual GDP in 2019 to official data in 2019. Our predictions are ~98% similar to official stats across ~1300 regions. Use the dashboard to see our accuracy rates per country and per region.
We use satellite imagery, machine learning, luminosity, and other inputs to calculate subnational GDP.
Our department is ranked #1 in Economic Geography worldwide, and our methods build off an approach from the American Economic Review, the world’s top Economics journal. We were funded by the European Space Agency BIC UK.
We’re a team of Fellows, PhDs, and postdocs from the LSE whose research specializes in this field, and we’re bringing our findings into industry.
In benchmarking tests in the EU, our methods average ~98% accuracy.
We use NASA satellite imagery, luminosity, machine learning, and other inputs to estimate GDP.
We are Fellows/ PhDs/ Postdocs from the London School of Economics, initially funded by the European Space Agency BIC UK. We have published in peer-reviewed journals, are Forbes 30 under 30 entrepreneurs, and designed policies for national governments. We are complemented by ex-SoftBank and ex-McKinsey advisors.
PhD Economic Geography, LSE
Visiting Fellow, LSE
Forbes 30 under 30
PhD Economic Geography, LSE
Download data for 25 countries and 1297 regions for each month in 2015. You get 15,564 rows of data, broken down into the following fields.
|nuts0||Admin level 0 (country-level, highest level of granularity)||Italy||String|
|nuts1||Admin level 1||NORD-OVEST||String|
|nuts2||Admin level 2||Piemonte||String|
|nuts3||Admin level 3 (lowest level of granularity)||Cuneo||String|
|GDP_nuts3||GDP of Admin level 3 in millions of euros||1519.358||Float|
|MoM_GDP_Growth_nuts3||Percentage change of Admin level 3 GDP from last month||1.27||String|
|YoY_GDP_Growth_nuts3||Percentage change of Admin level 3 GDP from last year||3.07||String|
|GDP_nuts2||GDP of Admin level 2 in millions of euros||10768.4||Float|
|MoM_GDP_Growth_nuts2||Percentage change of Admin level 2 GDP from last month||1.14||String|
|YoY_GDP_Growth_nuts2||Percentage change of Admin level 2 GDP from last year||2.95||String|
|GDP_nuts1||GDP of Admin level 1 in millions of euros||46024.89||Float|
|MoM_GDP_Growth_nuts1||Percentage change of Admin level 1 GDP from last month||1.23||String|
|YoY_GDP_Growth_nuts1||Percentage change of Admin level 1 GDP from last year||3.21||String|
|GDP_nuts0||GDP of Admin level 0 in millions of euros||140536.4||Float|
|MoM_GDP_Growth_nuts0||Percentage change of Admin level 0 GDP from last month||1.02||String|
|YoY_GDP_Growth_nuts0||Percentage change of Admin level 3 GDP from last year||2.91||String|
|id_nuts0||Unique identifier for nuts 0 (e.g. UK)||IT||String|
|id_nuts1||Unique identifier for nuts 1 (e.g. UKC)||ITC||String|
|id_nuts2||Unique identifier for nuts 2 (e.g. UKC1)||ITC1||String|
|id_nuts3||Unique identifier for nuts 3 (e.g. UKC11)||ITC16||String|
|time||Unique identifier for time series||13||Integer|